Saturday, 16 January 2016

WHAT IS MACHINE LEARNING

WHAT IS MACHINE LEARNING?

Hello everyone! Happy new year to you all. Sorry for the delay in making this
post, just started NYSC for real and believe me it's quite stressful but there have fun times too (I guess). Anyway, enough chit-chat let's get to the
topic of the day - What is machine learning? This for me is a good place to
start for anyone who has an interest in any topic (not just machine learning).
What really is the thing I am interested in? That's the first question that I
feel should be clearly answered. The objective of the post is to briefly define
machine learning and give some of its popular applications.

According to Wikipedia, machine learning explores the study and
construction of algorithms that can learn to make predictions from data rather
than following static program instructions. Let me explain, machine learning
uses data to make predictions. These predictions could be anything from the saying
what the weather will be tomorrow, to classifying a handwritten digit, recognizing
a picture or predicting what the price of an item will be given features of
said item.

All of the tasks just mentioned would be difficult to achieve using rigid
programming rules. For example, a classic problem in machine learning is
classification of hand-written digits. Suppose we wanted to define what the
digit '7' should look like, how would we do that? This would be difficult to do
because people have different ways of writing the number '7'. Trying to write
rules to define what the digit '7' is (or isn't) to a program would be
difficult. In this case, the best option would be for the program to 'learn' the
various parameters required to correctly classify a digit. To do this, we would
collect samples of hand-written digits (data) which we would now feed to a
machine learning algorithm. The output of this algorithm can now be used to
classify digits.

Now that you know what machine learning is, let's look at some of its major
uses (if you feel there others, please feel free to add them in the comments
section). Machine learning is used mainly for prediction like I mentioned
earlier. This can be further classified into:

i.Regression

ii.Classification

In regression, we use numbers to predict numbers. Let me use the popular
example of trying to predict the price of a house. Assume we trying to predict
the price of a house and that we also features (also called attributes) of this
house e.g. square footage, number of bedrooms, number of bathrooms, the year it
was built and so on. The task is given all these features (which are basically
numbers) can predict how much this house will sell for? (another number).

Classification is more like regression – the only difference in this case
is that we are trying to predict a class. Another popular example for
classification is spam filtering where we use features of an email such the
words in the email, sender’s name, sender’s IP address etc. to predict if the
email is spam or not. This is called binary classification because we trying to
predict which of two classes an email
(or the item to be classified) belongs to. Sometimes, there may be more than
two classes. In this case it’s called multi-class classification. A good example
is classification of hand-written digits where we try to predict if a digit
belongs of 1 out of a possible 10 classes.

Another application of machine learning I would to mention is in the area
of products recommendation. This application is used by extensively by
companies such as Amazon (to recommend what shoppers may like to buy) and
Netflix (to recommend movies to users). Machine learning also finds application
in areas such as image recognition and classification where neural networks are
used to recognize and /or classify an image.

I hope this post has clearly explained what machine learning is and its
application. Please feel free to drop a comment about anything that is unclear
to you. Thanks for reading my blog. Hope to see you soon. Cheers!!!